Dissecting cascade computational components in spiking neural networks.

Shanshan Jia,Dajun Xing,Jian K. Liu,Zhaofei Yu,Tianming Yang

doi:10.1371/journal.pcbi.1009640

Abstract

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.

Highlights

One of the cornerstones for developing novel algorithms of neural computation is to utilize different neuronal network structures extracted from experimental data
It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons
A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex

Summary

Introduction

One of the cornerstones for developing novel algorithms of neural computation is to utilize different neuronal network structures extracted from experimental data. The connectome, wiring diagrams, becomes an increasingly important topic, especially, for those relatively simple neuronal circuits that are well-studied, such as the retina [1,2,3,4,5,6]. Based on certain experimental techniques, the wiring diagram of neuronal connections has been identified for simple animal models, including Caenorhabditis elegans [7], Drosophila [8], and tadpole larva [9]. The question of how to obtain a functional and dynamical neuronal circuit has been studied experimentally [16] and computationally [17, 18] with great efforts in recent years. Spike-triggered non-negative matrix factorization (STNMF) is one of the methods proposed to infer the underlying structural components of the retina based on temporal sequences of spiking activities recorded in ganglion cells [17]. STNMF takes a step further to analyze the mapping between stimuli and neural responses leveraging neural spikes while leaving out non-responsive stimuli [17, 27], with an assist of spare coding, as neurons generally fire with a low rate of spikes [28]

Methods

Results

Discussion

Conclusion